chore: update code docs verifier workflow

.github/workflows/docs-code.yaml

@@ -38,14 +38,11 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install ./lavague-core
-        pip install ./lavague-integrations/contexts/lavague-contexts-gemini
-        pip install ./lavague-integrations/contexts/lavague-contexts-openai
-        pip install ./lavague-integrations/drivers/lavague-drivers-selenium
+        pip install ./lavague-sdk
         sudo apt-get install -y jq
         pip freeze | grep lavague
         git log | head -1
-        export DISABLE_LAVAGUE_ANIMATION=1
+        export LAVAGUE_TELEMETRY=NONE
 
     - name: Check code consistency with docs/index.md
       run: |

README.md

@@ -64,19 +64,10 @@ pip install lavague
 2. Use our framework to build a Web Agent and implement the objective:
 
 ```python
-from lavague.core import  WorldModel, ActionEngine
-from lavague.core.agents import WebAgent
-from lavague.drivers.selenium import SeleniumDriver
-
-selenium_driver = SeleniumDriver(headless=False)
-world_model = WorldModel()
-action_engine = ActionEngine(selenium_driver)
-agent = WebAgent(world_model, action_engine)
-agent.get("https://huggingface.co/docs")
-agent.run("Go on the quicktour of PEFT")
-
-# Launch Gradio Agent Demo
-agent.demo("Go on the quicktour of PEFT")
+from lavague.sdk import WebAgent
+
+agent = WebAgent()
+trajectory = agent.run("https://huggingface.co/docs", "Go on the quicktour of PEFT")
 ```
 
 For more information on this example and how to use LaVague, see our [quick-tour](https://docs.lavague.ai/en/latest/docs/get-started/quick-tour/).
@@ -156,33 +147,33 @@ The cost of these LLM calls depends on:
 
 Please see our [dedicated documentation on token counting and cost estimations](https://docs.lavague.ai/en/latest/docs/get-started/token-usage/) to learn how you can track all tokens and estimate costs for running your agents.
 
-## 📈 Data collection
-
-We want to build a dataset that can be used by the AI community to build better Large Action Models for better Web Agents. You can see our work so far on building community datasets on our [BigAction HuggingFace page](https://huggingface.co/BigAction).
-
-This is why LaVague collects the following user data telemetry by default:
-
-- Version of LaVague installed
-- Code / List of actions generated for each web action step
-- The past actions
-- The "observations" (method used to check the current page)
-- LLM used (i.e GPT4)
-- Multi modal LLM used (i.e GPT4)
-- Randomly generated anonymous user ID
-- Whether you are using a CLI command (lavague-qa for example), the Gradio demo or our library directly.
-- The objective used 
-- The chain of thoughts on the agent
-- The interaction zone on the page (bounding box)
-- The viewport size of your browser
-- The current step
-- The instruction(s) generated & the current engine used
-- The token costs & usages
-- The URL you performed an action on
-- Whether the action failed or succeeded
-- The extra used data specified
-- Error message, where relevant
-- The source nodes (chunks of HTML code retrieved from the web page to perform this action)
-
+## 📈 Data collection
+
+We want to build a dataset that can be used by the AI community to build better Large Action Models for better Web Agents. You can see our work so far on building community datasets on our [BigAction HuggingFace page](https://huggingface.co/BigAction).
+
+This is why LaVague collects the following user data telemetry by default:
+
+- Version of LaVague installed
+- Code / List of actions generated for each web action step
+- The past actions
+- The "observations" (method used to check the current page)
+- LLM used (i.e GPT4)
+- Multi modal LLM used (i.e GPT4)
+- Randomly generated anonymous user ID
+- Whether you are using a CLI command (lavague-qa for example), the Gradio demo or our library directly.
+- The objective used 
+- The chain of thoughts on the agent
+- The interaction zone on the page (bounding box)
+- The viewport size of your browser
+- The current step
+- The instruction(s) generated & the current engine used
+- The token costs & usages
+- The URL you performed an action on
+- Whether the action failed or succeeded
+- The extra used data specified
+- Error message, where relevant
+- The source nodes (chunks of HTML code retrieved from the web page to perform this action)
+
 **Be careful to NEVER includes personal information in your objectives and the extra user data. If you intend to includes personal information in your objectives/extra user data, it is HIGHLY recommended to turn off the telemetry.**
 
 ### 🚫 Turn off all telemetry